Should I apply ReLU to non negative output?
Applying a ReLU activation function to a non-negative number is the same as applying the identity activation function.

What you may wish to do is apply $\ln(|\vec{x}|)$, $\arctan(x[0], x[1])$, ..., $\arctan(x[0], x[N-1])$, where $N$ is the number of dimensions to $\vec{x}$, and pass this directly into a parameter matrix multiplication (which involves deliberately using an identity activation in the input layer in some frameworks). This typically improves speed, accuracy, and reliability of convergence when the magnitudes exhibit a distribution close to an exponential of Gaussian and the directional components are near Gaussian when expressed in radians.